Saved Queries

To address the limitations of Retrieval-Augmented Generation (RAG) systems in handling long policy documents, mitigating information dilution, and reducing hallucinations in engineering-oriented applications, this paper proposes SPR-RAG, a retrieval-augmented framework designed for knowledge-intensive vertical domains such as electric power policy analysis. With practicality and interpretability as core design goals, SPR-RAG introduces a Semantic Parsing Retriever (SPR), which integrates community detection–based entity disambiguation and transforms natural language queries into logical forms for structured querying over a domain knowledge graph, thereby retrieving verifiable triple-based evidence. To further resolve retrieval bias arising from diverse policy writing styles and inconsistencies between user queries and policy text expressions, a question-repository–based indirect retrieval mechanism is developed. By generating and matching latent questions, this module enables more robust retrieval of non-structured contextual evidence. The system then fuses structured and unstructured evidence into a unified dual-source context, providing the generator with an interpretable and reliable grounding signal. Experiments conducted on real electric power policy corpora demonstrate that SPR-RAG achieves 90.01% faithfulness—representing a 5.26% improvement over traditional RAG—and 76.77% context relevance, with a 5.96% gain. These results show that SPR-RAG effectively mitigates hallucinations caused by ambiguous entity names, textual redundancy, and irrelevant retrieved content, thereby improving the verifiability and factual grounding of generated answers. Overall, SPR-RAG demonstrates strong deployability and cross-domain transfer potential through its “Text → Knowledge Graph → RAG” engineering paradigm. The framework provides a practical and generalizable technical blueprint for building high-trust, industry-grade question–answering systems, offering substantial engineering value and real-world applicability. Full article

(This article belongs to the Section Algorithms for Multidisciplinary Applications)

18 pages, 2426 KB

Open AccessArticle

Enhanced YOLOv8n-Based Three-Module Lightweight Helmet Detection System

by Xinyu Zuo, Yiqing Dai, Chao Yu and Wang Gang

Sensors 2025, 25(24), 7664; https://doi.org/10.3390/s25247664 (registering DOI) - 17 Dec 2025

Abstract

Maintaining a safe working environment for construction workers is critical to the improvement of urban areas. Several issues plague the present safety helmet detection technologies utilized on construction sites. Some of these issues include low accuracy, expensive deployment of edge devices, and complex backgrounds. To overcome these obstacles, this paper introduces a detection method that is both efficient and based on an improved version of YOLOv8n. Three components make up the superior algorithm: the C2f-SCConv architecture, the Partial Convolutional Detector (PCD), and Coordinate Attention (CA). Detection, redundancy reduction, and feature localization accuracy are all improved with coordinate attention. To further enhance feature quality, decrease computing cost, and make corrections more effective, a Partial Convolution detector is subsequently constructed. Feature refinement and feature representation are made more effective by using C2f-SCConv instead of the bottleneck C2f module. In comparison to its predecessor, the upgraded YOLOv8n is superior in every respect. It reduced model size by 2.21 MB, increased frame rate by 12.6 percent, decreased FLOPs by 49.9 percent, and had an average accuracy of 94.4 percent. This method is more efficient, quicker, and cheaper to set up on-site than conventional helmet-detection algorithms. Full article

(This article belongs to the Special Issue Intelligent Sensors and Artificial Intelligence in Building)

22 pages, 3521 KB

Open AccessArticle

Energy-Model-Based Global Path Planning for Pure Electric Commercial Vehicles Toward 3D Environments

by Kexue Lai, Dongye Sun, Binhao Xu, Feiya Li, Yunfei Liu, Guangliang Liao and Junhang Jian

Machines 2025, 13(12), 1151; https://doi.org/10.3390/machines13121151 (registering DOI) - 17 Dec 2025

Abstract

Traditional path planning methods primarily optimize distance or time, without fully considering the impact of slope gradients in park road networks, variations in vehicle load capacity, and braking energy recovery characteristics on the energy consumption of pure electric commercial vehicles. To address these issues, this paper proposes a globally optimized path planning method based on energy consumption minimization. The proposed method introduces a multi-factor coupled energy consumption model for pure electric commercial vehicles, integrating slope gradients, load capacity, motor efficiency, and energy recovery. Using this vehicle energy consumption model and the park road network topology map, an energy consumption topology map representing energy consumption between any two nodes is constructed. An energy-optimized improved ant colony optimization algorithm (E-IACO) is proposed. By introducing an exponential energy consumption heuristic factor and an enhanced pheromone update mechanism, it prioritizes energy-saving path exploration, thereby effectively identifying the optimal energy consumption path within the constructed energy consumption topology map. Simulation results demonstrate that in typical three-dimensional industrial park scenarios, the proposed energy-optimized path planning method achieves maximum reductions of 10.57% and 4.90% compared to the A* algorithm and ant colony optimization (ACO), respectively, with average reductions of 5.14% and 1.97%. It exhibits excellent stability and effectiveness across varying load capacities. This research provides a reliable theoretical framework and technical support for reducing logistics operational costs in industrial parks and enhancing the economic efficiency of pure electric commercial vehicles. Full article

(This article belongs to the Section Vehicle Engineering)

30 pages, 5730 KB

Open AccessArticle

Blockchain-Based Platform for Secure Second-Hand Housing Trade: Requirement Identification, Functions Analysis, and Prototype Development

by Yi-Hsin Lin, Zhicong Hou, Jun Zhang, Xingyu Tao, Jack C. P. Cheng and Heng Li

Buildings 2025, 15(24), 4563; https://doi.org/10.3390/buildings15244563 (registering DOI) - 17 Dec 2025

Abstract

Most current second-hand housing sales, contract signing, and other processes require the participation of intermediaries. However, suppose the intermediary refuses to disclose all information to the parties involved in the transactions. In that case, this traditional model can lead to weak supervision and punishment, adverse selection, moral hazards, and weak contract enforcement. Blockchain technology can not only secure the information intermediaries share, encouraging them to disclose information, but can also generate irreversible records of housing transactions for data traceability. Therefore, this study aims to develop a framework based on blockchain technology for the trading of second-hand housing. In this study, a second-hand housing online trading framework (SHHOTF) based on smart contract development is proposed for the second-hand housing business process, aiming to promote second-hand housing transactions. The contributions of this study lie in (1) determining the framework requirements, (2) proposing the functional module of a framework based on the blockchain and designing a complete business process, (3) developing an architecture for integrating blockchain and second-hand housing transaction processes, and developing technical components that support the framework functions, and (4) demonstrating the use case in Britain, analyzing the effectiveness and innovation of the framework. Furthermore, the framework demonstrated a 24% increase in transaction speed compared to the traditional Ethereum public network. The proposed process is highly adaptable within the current second-hand housing domain, and the developed framework can serve as a reference for introducing blockchain technology into other industries or application scenarios. Full article

(This article belongs to the Special Issue BIM and Smart Technologies in Building Design, Construction, and Lifecycle Management)

►▼ Show Figures

Figure 1

26 pages, 1243 KB

Open AccessArticle

Dual-Tower TTP Semantic Matching Method Based on Soft–Hard Label Supervision and Gated Binary Interaction

by Zhenghao Qian, Fengzheng Liu, Mingdong He, Bo Li and Yinghai Zhou

Electronics 2025, 14(24), 4958; https://doi.org/10.3390/electronics14244958 (registering DOI) - 17 Dec 2025

Abstract

Existing methods for identifying Tactics, Techniques, and Procedures (TTPs) from complex cyber-attack descriptions face three core challenges: (1) severe semantic asymmetry between unstructured attack narratives and standardized TTP definitions; (2) continuously distributed semantic relations that cannot be fully captured by hard-label supervision; and (3) an open, long-tailed TTP taxonomy that impairs model generalization. To address these limitations, we introduce DTGBI-TM, a lightweight dual-tower semantic matching framework that integrates soft-label supervision, hierarchical hard-negative sampling, and gated binary interaction modeling. The model separately encodes attack descriptions and TTP definitions and employs a gated interaction module to adaptively fuse shared and divergent semantics, enabling fine-grained asymmetric alignment. A confidence-guided soft–hard collaborative supervision mechanism unifies weighted classification, semantic regression, and contrastive consistency into a multi-objective loss, dynamically rebalancing gradients to mitigate long-tail effects. Leveraging ATT & CK hierarchical priors, the model further performs in-tactic and cross-tactic hard-negative sampling to enhance semantic discrimination. Experiments on a real-world corpus demonstrate that DTGBI-TM achieves 98.53% F1 in semantic modeling and 79.77% Top-1 accuracy in open-set TTP prediction, while maintaining high inference efficiency and scalability in deployment. Full article

30 pages, 2433 KB

Open AccessArticle

Study on the Properties of a Polyvinyl Alcohol-Modified Ultrafine Cement Grouting Material for Weathered Zone Coal Seams

by Yanxiang Wen, Lijun Han, Yanlong Liu, Zishuo Liu, Maolin Tian and Benliang Deng

Sustainability 2025, 17(24), 11341; https://doi.org/10.3390/su172411341 - 17 Dec 2025

Abstract

The overlying rock in the weathering and oxidation zone has well-developed micro-fissures, making roadway roof control highly challenging. Ordinary cement slurry is hard to inject, failing to achieve effective reinforcement. By introducing admixtures like ultrafine fly ash and polyvinyl alcohol (PVA) to modify ultrafine cement, this paper developed a PVA-modified ultrafine cement-based grouting material (PVAM-UFCG). It systematically investigated the influences of various factors on the slurry’s setting time, fluidity, water separation rate, viscosity, and 28-day uniaxial compressive strength, determining the optimal mix ratio through comprehensive analysis. The results show that the water–cement ratio is the dominant factor affecting slurry viscosity, strength, and setting time; the polycarboxylate superplasticizer concentration has the most significant influence on fluidity and water separation rate; a 20% ultrafine fly ash replacement rate can optimize particle gradation and enhance long-term strength; and a 1.0% polyvinyl alcohol concentration can effectively control the water separation rate (≤5%) and improve slurry cohesiveness. Through range analysis and multi-indicator comprehensive evaluation based on the entropy weight method, the performance-balanced optimal mix ratio meeting the grouting requirements for the Weathering and Oxidation Zone was determined: a water–cement ratio of 0.6, an ultrafine fly ash replacement rate of 20%, a polyvinyl alcohol concentration of 1.0%, and a polycarboxylate superplasticizer concentration of 0.4%. This mix ratio material exhibits good permeability, stability, and appropriate reinforcement strength. The research results can provide a new material choice and theoretical basis for controlling the surrounding rock of roadways under similar geological conditions. Full article

(This article belongs to the Topic Advances in Coal Mine Disaster Prevention Technology)

33 pages, 1798 KB

Open AccessArticle

Analyzing Parameter-Efficient Convolutional Neural Network Architectures for Visual Classification

by Nazmul Shahadat and Anthony S. Maida

Sensors 2025, 25(24), 7663; https://doi.org/10.3390/s25247663 - 17 Dec 2025

Abstract

Advances in visual recognition have relied on increasingly deep and wide convolutional neural networks (CNNs), which often introduce substantial computational and memory costs. This review summarizes recent progress in parameter-efficient CNN design across three directions: hypercomplex representations with cross-channel weight sharing, axial attention mechanisms, and real-valued architectures using separable convolutions. We highlight how these approaches reduce parameter counts while maintaining or improving accuracy. We further analyze our contributions within this landscape. Full hypercomplex neural networks (FHNNs) employ hypercomplex layers throughout the architecture to reduce latency and parameters, while representational axial attention models (RepAA) extend this efficiency by generating additional feature representations. To mitigate the remaining overhead of spatial hypercomplex operations, we introduce separable hypercomplex networks (SHNNs), which factorize quaternion convolutions into sequential vectormap operations, lowering parameters by approximately

50 %

. Finally, we compare these models with popular efficient architectures, such as MobileNets and SqueezeNets, and demonstrate that our residual one-dimensional convolutional networks (RCNs) achieve competitive performance in image classification and super-resolution with significantly fewer parameters. This review highlights emerging strategies for reducing computational overhead in CNNs and outlines directions for future research. Full article

(This article belongs to the Special Issue Deep Learning for Perception and Recognition Based on Sensor Data: Methods and Applications, 2nd Edition)

►▼ Show Figures

Figure 1

24 pages, 2210 KB

Open AccessArticle

Deep Transfer Learning for UAV-Based Cross-Crop Yield Prediction in Root Crops

by Suraj A. Yadav, Yanbo Huang, Kenny Q. Zhu, Rayyan Haque, Wyatt Young, Lorin Harvey, Mark Hall, Xin Zhang, Nuwan K. Wijewardane, Ruijun Qin, Max Feldman, Haibo Yao and John P. Brooks

Remote Sens. 2025, 17(24), 4054; https://doi.org/10.3390/rs17244054 - 17 Dec 2025

Abstract

Limited annotated data often constrain accurate yield prediction in underrepresented crops. To address this challenge, we developed a cross-crop deep transfer learning (TL) framework that leverages potato (Solanum tuberosum L.) as the source domain to predict sweet potato (Ipomoea batatas L.) yield using multi-temporal uncrewed aerial vehicle (UAV)-based multispectral imagery. A hybrid convolutional–recurrent neural network (CNN–RNN–Attention) architecture was implemented with a robust parameter-based transfer strategy to ensure temporal alignment and feature-space consistency across crops. Cross-crop feature migration analysis showed that predictors capturing canopy vigor, structure, and soil–vegetation contrast exhibited the highest distributional similarity between potato and sweet potato. In comparison, pigment-sensitive and agronomic predictors were less transferable. These robustness patterns were reflected in model performance, as all architectures showed substantial improvement when moving from the minimal 3 predictor subset to the 5–7 predictor subsets, where the most transferable indices were introduced. The hybrid CNN–RNN–Attention model achieved peak accuracy (

R^{2} \approx 0.64

and RMSE ≈ 18%) using time-series data up to the tuberization stage with only 7 predictors. In contrast, convolutional neural network (CNN), bidirectional gated recurrent unit (BiGRU), and bidirectional long short-term memory (BiLSTM) baseline models required 11–13 predictors to achieve comparable performance and often showed reduced or unstable accuracy at higher dimensionality due to redundancy and domain-shift amplification. Two-way ANOVA further revealed that cover crop type significantly influenced yield, whereas nitrogen rate and the interaction term were not significant. Overall, this study demonstrates that combining robustness-aware feature design with hybrid deep TL model enables accurate, data-efficient, and physiologically interpretable yield prediction in sweet potato, offering a scalable pathway for applying TL in other underrepresented root and tuber crops. Full article

(This article belongs to the Special Issue Application of UAV Images in Precision Agriculture)

►▼ Show Figures

Figure 1

16 pages, 1164 KB

Open AccessArticle

Adaptive Backstepping Control for Battery Pole Strip Mill Systems with Friction and Dead-Zone Input Nonlinearities

by Gengting Qiu, Yujie Hao, Gexin Chen, Guishan Yan and Yao Chen

Actuators 2025, 14(12), 618; https://doi.org/10.3390/act14120618 - 17 Dec 2025

Abstract

The dead-zone input and hydraulic cylinder friction of the pump-controlled automatic gauge control (AGC) system introduce significant challenges to the high-precision rolling of lithium battery pole pieces. To address these nonlinearities, this paper establishes the friction and dead-zone model of the pump-controlled AGC system, and a slide-mode observer is designed to estimate the friction state z in the LuGre model. Furthermore, an adaptive compensation method is adopted to identify the unknown parameters of the input dead-zone and friction models. Meanwhile, combined with the framework of backstepping control design, both matched and mismatched disturbances are effectively compensated. Stability analysis guarantees the convergence of the estimation errors and closed-loop signal boundedness. Finally, experimental results validate the effectiveness and robustness of the proposed control strategy. Full article

(This article belongs to the Special Issue High-Performance Control of Electromechanical Servo System Based on Motor/Hydraulic Actuator)

23 pages, 11824 KB

Open AccessArticle

Modeling Ice Detachment Events on Cryopumps During Space Propulsion Ground Testing

by Andreas Neumann

Aerospace 2025, 12(12), 1114; https://doi.org/10.3390/aerospace12121114 - 17 Dec 2025

Abstract

At DLR’s electric space propulsion vacuum test facility in Goettingen, spontaneous pressure rise events were observed, which led to interruptions of thruster testing. This study investigates the causes of four such events and presents a model that is able to simulate pressure rise events due to xenon ice sheet detachment from operating cryogenic pumps. The model results show good agreement with the observed pressure curves and can reproduce the pressure rise slope, event duration, down slope, and maximum pressure during these events. The masses of the detached xenon ice sheets are in the range from 2 g to 0.4 kg, which is reasonable with respect to the amount of ice on cryopump cold plates. This first modeling step is based on a phenomenological approach, but the good results show that it is worth expanding and refining the model, e.g., by introducing more ice shape options, adding ice bonding layer properties, and adding other gases and physical condensate properties. Full article

(This article belongs to the Section Astronautics & Space Science)

23 pages, 361 KB

Open AccessArticle

BiHom–Lie Brackets and the Toda Equation

by Botong Gai, Chuanzhong Li, Jiacheng Sun, Shuanhong Wang and Haoran Zhu

Symmetry 2025, 17(12), 2176; https://doi.org/10.3390/sym17122176 - 17 Dec 2025

Abstract

We introduce a BiHom-type skew-symmetric bracket on general linear Lie algebra

G L (V)

built from two commuting inner automorphisms

α = A d_{ψ}

and

β = A d_{ϕ}

, with [...] Read more.

We introduce a BiHom-type skew-symmetric bracket on general linear Lie algebra

G L (V)

built from two commuting inner automorphisms

α = A d_{ψ}

and

β = A d_{ϕ}

, with

ψ, ϕ \in G L (V)

and integers

i, j

. We prove that

(G L (V), {[\cdot, \cdot]}_{(ψ, ϕ)}^{(i, j)}, α, β)

is a BiHom–Lie algebra, and we study the Lax equation obtained by replacing the commutator in the finite nonperiodic Toda lattice by this bracket. For the symmetric choice

ϕ = ψ

with

(i, j) = (0, 0)

, the deformed flow is equivariant under conjugation and becomes gauge-equivalent, via

\tilde{L} = ψ^{- 1} L ψ

, to a Toda-type Lax equation with a conjugated triangular projection. In particular, scalar deformations amount to a constant rescaling of time. On embedded

2 \times 2

blocks, we derive explicit trigonometric and hyperbolic formulae that make symmetry constraints (e.g., tracelessness) transparent. In the asymmetric hyperbolic case, we exhibit a trace obstruction showing that the right-hand side is generically not a commutator, which amounts to symmetry breaking of the isospectral property. We further extend the construction to the weakly coupled Toda lattice with an indefinite metric and provide explicit

2 \times 2

solutions via an inverse-scattering calculation, clarifying and correcting certain formulas in the literature. The classical Toda dynamics are recovered at special parameter values. Full article

(This article belongs to the Special Issue Symmetry in Integrable Systems and Soliton Theories)

17 pages, 1806 KB

Open AccessArticle

Current Status of the Climate Change Impact Assessment System in Korea and Its Linkage with Urban Greenhouse Gas Observation for Sustainability: A Systematic Review and Case

by Sungwoon Jung and Jaewon Lee

Sustainability 2025, 17(24), 11339; https://doi.org/10.3390/su172411339 - 17 Dec 2025

Abstract

In 2022, Korea became the first country to introduce a climate change impact assessment (CCIA) system that requires prior analysis and evaluation of climate impacts for major development projects, delivering a relevant analysis and management framework for such purposes. This study reviews Korea’s CCIA system from a policy perspective, organizing its structural components, assessment procedures, and reporting methods according to the domains of greenhouse gas (GHG) mitigation and climate crisis adaptation. The system’s characteristics and assessment procedures of this system are also analyzed via a case study review of urban development projects. In the GHG mitigation category, emissions and absorptions should be investigated at each project stage and quantitative reduction amounts and targets established based on scientific and statistical evidence. Regarding climate crisis adaptation, regional climate risks should be analyzed and adaptation strategies for priority management areas developed based on impact prediction results. CO₂ concentrations recorded in Seoul’s central and background areas confirmed spatial differences in city-level GHG concentrations, proposing the CCIA’s potential practical use for enhancing future monitoring frameworks. To enhance the effectiveness of the CCIA and its consequences for future sustainability, the opinions of various stakeholders and linking the system with existing environmental impact (EIA) assessment frameworks are paramount. Full article

(This article belongs to the Section Air, Climate Change and Sustainability)

►▼ Show Figures

Figure 1

24 pages, 1976 KB

Open AccessArticle

EMS-YOLO-Seg: An Efficient Instance Segmentation Method for Lithium Mineral Under a Microscope Based on YOLO11-Seg

by Zhicheng Deng, Xiaofang Mei, Zeyang Qiu, Xueyu Huang and Zhenzhong Qiu

Appl. Sci. 2025, 15(24), 13239; https://doi.org/10.3390/app152413239 - 17 Dec 2025

Abstract

Lithium minerals are essential raw materials for new energy storage systems, and accurate instance segmentation of their microscopic images is crucial for efficient resource exploration and utilization. However, existing segmentation methods face challenges when processing lithium mineral images, including complex texture overlaps, missed detection of small particles, and deployment difficulties on edge devices, making it hard to balance segmentation accuracy with inference speed. To address these challenges, this paper proposes an efficient instance segmentation method based on YOLO11-seg, named EMS-YOLO-seg. First, we designed Multi-Scale Partial Convolution (MSPConv) and integrated it into the C3k2 module. The modified C3k2-MSP module optimizes the model’s receptive field and enhances its multi-scale feature extraction capability. We replaced the PSABlock module with the CBAM attention mechanism, introducing the C2PSA-CBAM module, which strengthens the model’s channel focus and feature extraction abilities. The redesigned Segment-LSCDMSP segmentation head reduces computational complexity and improves detection efficiency. Experimental results on our custom-built lithium mineral microscopic image dataset show that compared to the baseline YOLO11n-seg model, the EMS-YOLO-seg model achieved a 0.8% and 0.8% improvement in

m A P_{50}^{b o x}

and

m A P_{50 : 95}^{b o x}

, respectively, and a 1% and 0.7% improvement in

m A P_{50}^{m a s k}

and

m A P_{50 : 95}^{m a s k}

. Additionally, the model reduced the number of parameters by 52.1%, FLOPs by 18.6%, model size by 49.4%, and increased FPS by 12.7%. This study provides reliable technical support for accurate instance segmentation of lithium mineral microscopic images and demonstrates strong scene adaptability and promising potential for real-time deployment under industrial environments and resource-constrained scenarios. Full article

17 pages, 1974 KB

Open AccessArticle

Tunable Structural Color in Au-Based One-Dimensional Hyperbolic Metamaterials

by Ricardo Téllez-Limón, René I. Rodríguez-Beltrán, Fernando López-Rayón, Mauricio Gómez-Robles, Katie Figueroa-Guardiola, Jesús E. Chávez-Padua, Victor Coello and Rafael Salas-Montiel

Nanomaterials 2025, 15(24), 1898; https://doi.org/10.3390/nano15241898 - 17 Dec 2025

Abstract

Structural coloration arising from nanoscale light–matter interactions has emerged as a key research area in nanophotonics. Among the various materials investigated, noble metals—particularly gold—play a central role due to their well-defined plasmonic response and chemical stability, but their structural coloring typically requires complex and highly engineered nanostructures. However, modern photonic technologies demand scalable approaches to produce structural colors that can be finely tuned. In this contribution, we experimentally and numerically demonstrate the fine tunability of structural color in gold-based one-dimensional hyperbolic metamaterials (1D-HMMs) by varying their structural parameters: number of layers (N), period (T), and filling fraction (p). Our results show that variations in N lead to changes in luminance with minimal shifts in chromaticity, while variations in T introduce moderate color shifts without affecting luminance. In contrast, changes in p produce the largest modifications in chromaticity, though the trend is non-monotonic and less predictable. These findings highlight the potential of 1D-HMMs for achieving finely controlled gold-based coloration for advanced photonic technologies. Full article

(This article belongs to the Topic Nanomaterials for Photonics and Optoelectronics: Practical Applications and Advances)

►▼ Show Figures

Graphical abstract

19 pages, 1130 KB

Open AccessArticle

Toward Sustainable Mobility: A Hybrid Quantum–LLM Decision Framework for Next-Generation Intelligent Transportation Systems

by Nafaa Jabeur

Sustainability 2025, 17(24), 11336; https://doi.org/10.3390/su172411336 - 17 Dec 2025

Abstract

Intelligent Transportation Systems (ITSs) aim to improve mobility and reduce congestion, yet current solutions still struggle with scalability, sensing bottlenecks, and inefficient computational resource usage. These limitations impede the shift towards environmentally responsible mobility. This work introduces ORQCIAM (Orchestrated Reasoning based on Quantum Computing and Intelligence for Advanced Mobility), a modular framework that combines Quantum Computing (QC) and Large Language Models (LLMs) to enable real-time, energy-aware decision-making in ITSs. Unlike conventional ITS or AI-based approaches that focus primarily on traffic performance, ORQCIAM explicitly incorporates sustainability as a design objective, targeting reductions in travel time, fuel or energy consumption, and CO₂ emissions. The framework unifies cognitive, virtual, and federated sensing to enhance data reliability, while a hybrid decision layer dynamically orchestrates QC–LLM interactions to minimize computational overhead. Scenario-based evaluation demonstrates faster incident screening, more efficient routing, and measurable sustainability benefits. Across tested scenarios, ORQCIAM achieved 9–18% reductions in travel time, 6–14% lower estimated CO₂ emissions, and around a 50–75% decrease in quantum-optimization calls by concealing QC activation during non-critical events. These results confirm that dynamic QC–LLM coordination effectively decreases computational overhead while supporting greener and more adaptive mobility patterns. Overall, ORQCIAM illustrates how hybrid QC–LLM architectures can serve as catalysts for efficient, low-carbon, and resilient transportation systems aligned with sustainable smart-city goals. Full article

(This article belongs to the Special Issue Artificial Intelligence in Sustainable Transportation)

►▼ Show Figures

Figure 1

Show export options Show export options

Select all

Export citation of selected articles as:

Error

Oops... you haven't selected anything for export.

Displaying article 1-50 on page 1 of 2000.

Go to page 1 2 3 4 5

Search Results (102,668)

Further Information

Guidelines

MDPI Initiatives

Follow MDPI